Peer-Reviewed Articles
25 Map Registration of Image Sequences Using Linear
Features
Caixia Wang, Anthony Stefanidis, Arie Croitoru, and Peggy
Agouris
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This paper proposes an automatic and fast algorithm for
registering aerial image sequences to vector map data using
linear features as control information. Our method is based
on the extraction of linear features using active contour
models (also known as, snakes), followed by the construction
of a polygonal template upon which a matching process is
applied. To accommodate more robust matching, this work
presents both exact and inexact matching schemes for linear
features. Additionally, in order to overcome the influence of
the snakes-based extraction process on the matching results,
a matching refinement process is suggested. Using the
information derived from the matching process, we then
determine the transformation parameters between overlapping
images and generate a mosaic image sequence, which
can then be registered to a map. The performance of the
proposed scheme was tested on sequences of aerial imagery
of 1 m resolution that were subjected to shifts and rotations
using both the exact and inexact matching scheme, and was
shown to produce angular accuracy of less than 0.7 degrees
and positional accuracy of less than two pixels.
39 Cadastral Mapping of Forestlands in Greece: Current
and Future Challenges
Moschos Vogiatzis
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The Hellenic Cadastre Program (HCP) of Greece aims at
developing a modern cadastral system for the first time in
the Hellenic history. This paper is focused on the issues
related to cadastral forestlands digital mapping, an indispensable
part of HCP. Mapping the forestlands is a challenge
of multiple disciplines. It includes photogrammetry, photointerpretation,
Geographic Information Systems (GIS), and a
clear understanding of the current institutional and legislative
setting. The process requires both historical and current
information pertaining to land-cover in order to identify
forestland changes over time. Historical and current digital
orthoimagery is generated through photogrammetric operations.
Forestlands are delineated in a spatiotemporal environment;
state property rights in forestlands are allocated
and land ownership is established within the framework of
HCP. This paper demonstrates that the integration of airborne
remote sensing and collateral data with a GIS is an effective
approach for cadastral forest mapping. The produced GIS
databases and large-scale Forest Maps may serve as a data
foundation towards a land register of forests.
47 The Influence of DEM Accuracy on Topographic
Correction of Ikonos Satellite Images
Janet Nichol and Law Kin Hang
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There is no research which specifically investigates the
influence of the Digital Elevation Model (DEM) on topographic
correction of satellite images. Such an investigation is
necessary in view of the very high resolution (VHR) of recent
sensors such as Ikonos and QuickBird and the low availability
of accurate height information for the derivation of slope
and aspect values. A comparative study of multispectral
Ikonos images is presented using eight selected interpolation
techniques with contour data. The DEM influence was evaluated
by analyzing the variance and classification accuracy of
topographically corrected images using the different DEMS.
These were found to vary widely, with a 30 percent reduction
in variance and a 20 percent improvement in overall classification
accuracy between the worst and best performing
interpolation techniques. Furthermore, smoothing techniques
commonly used for the removal of noise in interpolated
contour data or in existing DEMs were found to offer no
significant improvement in intra-class variance or classification
accuracy, when used with a grid resolution compatible
with VHR images. However, a more rigorous planar slope
function was found to be more effective, and when combined
with the best interpolator, the natural neighbor, or Sibson,
method, very high classification accuracy was achieved in the
topographically corrected images.
55 Global Elevation Ancillary Data for Land-use
Classification Using Granular Neural Networks
Demetris Stathakis and Ioannis Kanellopoulos
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The development of digital global databases containing data
such as elevation and soil can greatly simplify and aid in the
classification of remotely sensed data to create land-use
classes. An efficient method that can simultaneously handle
diverse input dimensions can be formed by merging fuzzy
logic and neural networks. The so-called granular or fuzzy
neural networks are able not only to achieve high classification
levels, but at the same time produce compressed and
transparent neural network skeletons. Compression results
in reduced training times, while transparency is an aid for
interpreting the structure of the neural network by translating
it into meaningful rules and vice versa. The purpose of this
paper is to provide some initial guidelines for the construction
of granular neural networks in the remote sensing
context, while using global elevation ancillary data within
the classification process.
65 Using CASI Hyperspectral Imagery to Detect Mortality
and Vegetation Stress Associated with a New
Hardwood Forest Disease
Ruiliang Pu, Maggi Kelly, Gerald. L. Anderson, and Peng Gong
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A Compact Airborne Spectrographic Imager-2 (CASI) dataset
was used for detecting mortality and vegetation stress
associated with a new forest disease. We first developed a
multilevel classification scheme to improve classification
accuracy. Then, the CASI raw data were transformed to
reflectance and corrected for topography, and a principal
component (PC) transformation of all 48 bands and the
visible bands and NIR bands were separately conducted to
extract features from the CASI data. Finally, we classified the
calibrated and corrected CASI imagery using a maximum
likelihood classifier and tested the relative accuracies of
classification across the scheme. The multilevel scheme
consists of four levels (Levels 0 to 3). Level 0 covered the
entire study area, classifying eight classes (oak trees,
California bay trees, shrub areas, grasses, dead trees, dry
areas, wet areas, and water). At Level 1, the vegetated and
non-vegetated areas were separated. The vegetated and nonvegetated
areas were further subdivided into four vegetated
(oak trees, California bay trees, shrub areas, grasses) and
four non-vegetated (dead trees, dry areas, wet areas, and
water) classes at Level 2. Level 3 identified stressed and
non-stressed oak trees (two classes). The ten classes classified
at different levels are defined as final classes in this
study. The experimental results indicated that classification
accuracy generally increased as the detailed classification
level increased. When the CASI topographically corrected
reflectance data were processed into ten PCs (five PCs from
the visible region and five PCs from NIR bands), the classification
accuracy for Level 2 vegetated classes (non-vegetated
classes) increased to 80.15 percent (94.10 percent) from
78.07 percent (92.66 percent) at Level 0. The accuracy of
separating stressed from non-stressed oak trees at Level 3
was 75.55 percent. When classified as a part of Level 0, the
stressed and non-stressed were almost inseparable. Furthermore,
we found that PCs derived from visible and NIR bands
separately yielded more accurate results than the PCs from
all 48 CASI bands.
77 Origins of Aerial Photographic Interpretation, U.S.
Army, 1916 to 1918
James B. Campbell
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World War I formed the incubator for aerial reconnaissance
and photointerpretation. This study, based upon official
histories and archival materials, including correspondence,
reports, unit histories, and related documents, surveys the
development of photoreconnaissance as practiced by the
American Expeditionary Forces (AEF) during the period
1917 to 1919. The most visible advances were technologic
improvisations, developed in the field that integrated
the camera and the airplane to form, arguably, the most
effective intelligence resource of the conflict. Technological
innovations were accompanied by parallel developments in
organizational and training infrastructures necessary to
derive information for images acquired by these instruments.
Interpretation techniques developed from simple
annotations of oblique photographs acquired using handheld
cameras to sophisticated analyses of images acquired
by automatic cameras suspended in the aircraft fuselage.
As the war concluded, efforts were underway to develop
foundations for photogrammetric methods to derive accurate
planimetry, which later formed foundations for civil
applications of aerial photography.
95 A Permanent Test Field for Digital Photogrammetric
Systems
Eija Honkavaara, Jouni Peltoniemi, Eero Ahokas, Risto
Kuittinen, Juha Hyyppä, Juha Jaakkola, Harri Kaartinen, Lauri
Markelin, Kimmo Nurminen, and Juha Suomalainen
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Comprehensive field-testing and calibration of digital photogrammetric
systems are essential to characterize their performance,
to improve them, and to be able to use them for
optimal results. The radiometric, spectral, spatial, and geometric
properties of digital systems require calibration and testing.
The Finnish Geodetic Institute has maintained a permanent test field for geometric, radiometric, and spatial resolution calibration and testing of high-resolution airborne and satellite imaging systems in Sjökulla since 1994. The special features of this test field are permanent resolution and reflectance targets made of gravel. The Sjökulla test field with some supplementary targets is a prototype for a future photogrammetric field calibration site.
This article describes the Sjökulla test field and its construction and spectral properties. It goes on to discuss targets and methods for system testing and calibration, and highlights the calibration and testing of digital photogrammetric systems.
107 Automated Geometric Correction of High-resolution
Pushbroom Satellite Data
Marco Gianinetto and Marco Scaioni
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In this article, we present the use of the Automatic Ground
control points Extraction technique (AGE) for increasing the
automation in the geometric correction of high-resolution
satellite imagery. The method is based on an image-to-image
matching between the satellite data and an already geocoded
image (i.e., a digital orthophoto). By using an adaptive least
squares matching algorithm which implements a very robust
outlier rejection technique, AGE can automatically measure
many hundreds of topographic features (TFs) on the images,
whose cartographic coordinates are derived from the geocoded
image and elevations are extracted from an associated
digital elevation model (DEM). The AGE technique has been
tested for different high-resolution data: (a) 0.62-meter
QuickBird panchromatic data (basic imagery processing
level), (b) 2.5-meter SPOT-5/HRG panchromatic supermode
data (standard 1B processing level), and (c) 1-meter Ikonos
panchromatic data (standard Geo product processing level)
collected in the Northern of Italy, both in flat (Torino Caselle
test site) and mountain areas (Lecco test site). Regardless
the relative image resolution between the satellite and the
aerial data (1-meter) and regardless the processing level of
the original satellite data, a similar TFs density has been
obtained for both the QuickBird and the SPOT-5/HRG data
(4.4 GCPs/km2 and 4.1 GCPs/km2) respectively, with a geometric
accuracy for the GCPs extracted of 0.90 m for QuickBird
and 3.90 m for SPOT-5/HRG. For the Ikonos imagery, AGE
extracted a more dense set of GCPs (8.7 GCPs/km2) but with
a lower accuracy (3.19 m). The TFs identified with AGE can
be used as GCPs for the rational polynomial coefficients
(RPCs) computation and, therefore, for implementing a full
automatic orthoimage generation procedure. By using the
commercial off-the-shelf software PCI Geomatica® v.9.1,
orthoimages have been generated for all datasets. The
geometric accuracy was verified on a set of 30 manually
measured independent check points (CPs) and assessed
a precision of 4.99 m RMSE for QuickBird, 5.99 m RMSE
for SPOT-5/HRG, and 8.65 m RMSE for Ikonos. The use of a
non-conventional image orthorectification technique implementing
a neural network GCPs regularization, tested for
the SPOT-5/HRG data, showed the full potential of the AGE
method, allowing to obtain a 3.83 m RMSE orthoprojection
in a fully automated way.
117 Agricultural Monitoring Using Envisat Alternating
Polarization SAR Images
Mika Karjalainen, Harri Kaartinen, and Juha Hyyppä
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In agricultural remote sensing, applied images should be
acquired frequently enough in order to monitor important
crop growth stages. Thanks to the cloud penetrating and
flexible swath-positioning capabilities of space-borne SAR at
present, images can be acquired even at the interval of few
days during a growing season. In this study, dual-polarization
(VV/VH) Envisat SAR images with high a temporal resolution
were used in association with limited ancillary data to
monitor crop growth and to classify crop species. It was
noticed that the high temporal resolution enabled nearly
continuous monitoring, but it also caused problems because
of the varying incidence angles. Moreover, to carry out field
surveys rapidly enough for research purposes was observed as
a problem. An R2 of 0.55 was obtained for estimating the crop
growth, when average crop height in parcels was used to
describe the amount of biomass. An overall accuracy of
74.7 percent was achieved for crop species classification.
Envisat VH polarization appeared to be useful in the estimation,
even though, the noise equivalent σ0 was too high to
detect early crop growth. Field-based averaging was required,
thus, for example for precision farming purposes a better
spatial resolution would be needed to detect biomass variations
within parcels.